{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T17:27:41Z","timestamp":1790443661312,"version":"4.1.0"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:00:00Z","timestamp":1780099200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":72,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100020912","name":"King Faisal University","doi-asserted-by":"publisher","award":["KFU261717"],"award-info":[{"award-number":["KFU261717"]}],"id":[{"id":"10.13039\/501100020912","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Leukemia, a life-threatening hematological malignancy, necessitates early and accurate diagnosis for effective treatment. Traditional microscopic examination is often time-consuming and subjective, while most existing deep learning-based studies are limited to a narrow selection of models and lack interpretability analysis. To address these gaps, this study presents a comprehensive comparative evaluation of fourteen pre-trained CNN architectures, including EfficientNetB1\/B3\/B5, Xception, InceptionV3, InceptionResNetV2, DenseNet201, ResNet variants, VGG16\/19, and MobileNet, for automated leukemia detection from microscopic blood smear images. A publicly available Kaggle dataset containing 10,700 labeled images with variations in staining intensity, cell morphology, and image resolution was used. The dataset was divided into training (70%, 7500 images), validation (15%, 1600 images), and testing (15%, 1600 images) subsets using stratified sampling to preserve class balance. To ensure statistical robustness, all experiments were repeated ten times with different random seeds, and performance metrics are reported as mean values, with pairwise McNemar\u2019s tests applied to validate the significance of observed differences (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05). EfficientNetB1, EfficientNetB5, Xception, and ResNet50 achieved test accuracies of up to 96%, recall up to 98%, F1-scores up to 97%, and AUC values up to 0.987, indicating excellent threshold-independent discriminative capability. Explainable AI techniques, specifically Grad-CAM and LIME, were integrated to enhance transparency and clinical trustworthiness. Comparative analysis demonstrates competitive or superior performance against recent state-of-the-art methods, providing preliminary evidence toward future clinical translation. However, the study is limited by reliance on a single publicly available binary-class dataset, which constrains generalizability across imaging devices, staining protocols, and clinical sites; external validation on independent multi-center datasets is therefore identified as a primary direction for future work.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-55415-x","type":"journal-article","created":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T05:34:40Z","timestamp":1780119280000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automated leukemia detection from microscopic images using deep transfer learning with explainable AI-based analysis"],"prefix":"10.1038","volume":"16","author":[{"given":"Md","family":"Ashikuzzaman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mir","family":"Md. Julhash","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Feroz","family":"Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Shafiul","family":"Alam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Imil Hamda","family":"Imran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Obaidullah","family":"Obaidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Kamrul","family":"Islam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,30]]},"reference":[{"key":"55415_CR1","first-page":"229","volume":"74","author":"F Bray","year":"2024","unstructured":"Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 74, 229\u2013263 (2024).","journal-title":"CA Cancer J. Clin."},{"key":"55415_CR2","unstructured":"Cancer. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cancer."},{"key":"55415_CR3","doi-asserted-by":"crossref","unstructured":"Zhu, W., Xie, L., Han, J. & Guo, X. The application of deep learning in cancer prognosis prediction. Cancers 12 (2020).","DOI":"10.3390\/cancers12030603"},{"key":"55415_CR4","doi-asserted-by":"crossref","unstructured":"Baig, R., Rehman, A., Almuhaimeed, A., Alzahrani, A. & Rauf, H. T. Detecting malignant leukemia cells using microscopic blood smear images: A deep learning approach. Appl Sci 12, (2022).","DOI":"10.3390\/app12136317"},{"key":"55415_CR5","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/0010-4825(88)90033-9","volume":"18","author":"ES Gelsema","year":"1988","unstructured":"Gelsema, E. S., Bao, H. F., Smeulders, A. W. M. & den Harink, H. C. Application of the method of multiple thresholding to white blood cell classification. Comput. Biol. Med. 18, 65\u201374 (1988).","journal-title":"Comput. Biol. Med."},{"key":"55415_CR6","doi-asserted-by":"crossref","unstructured":"Das, S. K., Islam, K. S., Neha, T. A., Khan, M. M. & Bourouis, S. Towards the segmentation and classification of white blood cell cancer using hybrid mask-recurrent neural network and transfer learning. Contrast Media Mol. Imaging 4954854 (2021).","DOI":"10.1155\/2021\/4954854"},{"key":"55415_CR7","doi-asserted-by":"publisher","first-page":"81741","DOI":"10.1109\/ACCESS.2022.3196037","volume":"10","author":"PK Das","year":"2022","unstructured":"Das, P. K., Meher, A. D. V., Panda, S., Abraham, A. & R. & A Systematic Review on Recent Advancements in Deep and Machine Learning Based Detection and Classification of Acute Lymphoblastic Leukemia. IEEE Access. 10, 81741\u201381763 (2022).","journal-title":"IEEE Access."},{"key":"55415_CR8","doi-asserted-by":"crossref","unstructured":"El Houby & M.f, E. Acute lymphoblastic leukemia diagnosis using machine learning techniques based on selected features. Sci. Rep. 15, 28056 (2025).","DOI":"10.1038\/s41598-025-12361-4"},{"key":"55415_CR9","doi-asserted-by":"publisher","first-page":"23782","DOI":"10.1038\/s41598-025-05585-x","volume":"15","author":"S Kasim","year":"2025","unstructured":"Kasim, S. et al. Multiclass leukemia cell classification using hybrid deep learning and machine learning with CNN-based feature extraction. Sci. Rep. 15, 23782 (2025).","journal-title":"Sci. Rep."},{"key":"55415_CR10","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1007\/s00779-020-01467-3","volume":"27","author":"PK Mallick","year":"2023","unstructured":"Mallick, P. K., Mohapatra, S. K., Chae, G. S. & Mohanty, M. N. Convergent learning\u2013based model for leukemia classification from gene expression. Pers. Ubiquitous Comput. 27, 1103\u20131110 (2023).","journal-title":"Pers. Ubiquitous Comput."},{"key":"55415_CR11","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.medengphy.2021.10.006","volume":"98","author":"KK Anilkumar","year":"2021","unstructured":"Anilkumar, K. K., Manoj, V. J. & Sagi, T. M. Automated detection of leukemia by pretrained deep neural networks and transfer learning: A comparison. Med. Eng. Phys. 98, 8\u201319 (2021).","journal-title":"Med. Eng. Phys."},{"key":"55415_CR12","doi-asserted-by":"crossref","unstructured":"Loey, M., Naman, M. & Zayed, H. Deep transfer learning in diagnosing leukemia in blood cells. Computers 9, (2020).","DOI":"10.3390\/computers9020029"},{"key":"55415_CR13","doi-asserted-by":"publisher","unstructured":"Jagadev, P. & Virani, H. G. Detection of leukemia and its types using image processing and machine learning. In International Conference on Trends in Electronics and Informatics (ICEI) 522\u2013526 (2017). https:\/\/doi.org\/10.1109\/ICOEI.2017.8300983","DOI":"10.1109\/ICOEI.2017.8300983"},{"key":"55415_CR14","doi-asserted-by":"publisher","first-page":"01111","DOI":"10.1051\/e3sconf\/202130901111","volume":"309","author":"MJ Ahmed","year":"2021","unstructured":"Ahmed, M. J. & Nayak, P. A. Survey on Leukemia Detection using Image Processing Techniques. E3S Web Conf. 309, 01111 (2021).","journal-title":"E3S Web Conf."},{"key":"55415_CR15","doi-asserted-by":"publisher","first-page":"3105","DOI":"10.1007\/s40747-021-00473-z","volume":"8","author":"S Saleem","year":"2022","unstructured":"Saleem, S. et al. A deep network designed for segmentation and classification of leukemia using fusion of the transfer learning models. Complex. Intell. Syst. 8, 3105\u20133120 (2022).","journal-title":"Complex. Intell. Syst."},{"key":"55415_CR16","doi-asserted-by":"publisher","unstructured":"Aftab, M. O., Javed Awan, M., Khalid, S., Javed, R. & Shabir, H. Executing Spark BigDL for leukemia detection from microscopic images using transfer learning. In 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA) 216\u2013220. https:\/\/doi.org\/10.1109\/CAIDA51941.2021.9425264 (2021).","DOI":"10.1109\/CAIDA51941.2021.9425264"},{"key":"55415_CR17","doi-asserted-by":"publisher","first-page":"38042","DOI":"10.1038\/s41598-025-21888-5","volume":"15","author":"A Uppal","year":"2025","unstructured":"Uppal, A., Kakkar, B., Johri, P., Kumar, Y. & Koul, A. Enhancing lymphoma cancer detection using deep transfer learning on histopathological images. Sci. Rep. 15, 38042 (2025).","journal-title":"Sci. Rep."},{"key":"55415_CR18","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/j.engappai.2018.04.024","volume":"72","author":"LHS Vogado","year":"2018","unstructured":"Vogado, L. H. S., Veras, R. M. S., Araujo, F. H. D., Silva, R. R. V. & Aires, K. R. T. Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification. Eng. Appl. Artif. Intell. 72, 415\u2013422 (2018).","journal-title":"Eng. Appl. Artif. Intell."},{"key":"55415_CR19","doi-asserted-by":"crossref","unstructured":"Ansari, S., Navin, A. H., Sangar, A. B., Gharamaleki, J. V. & Danishvar, S. A customized efficient deep learning model for the diagnosis of acute leukemia cells based on lymphocyte and monocyte images. Electronics 12 (2023).","DOI":"10.3390\/electronics12020322"},{"key":"55415_CR20","doi-asserted-by":"publisher","first-page":"8661","DOI":"10.1038\/s41598-025-92513-8","volume":"15","author":"A Bertolini","year":"2025","unstructured":"Bertolini, A. et al. Immature forms of low density granulocytes are increased in acute myeloid leukemia and myelodysplastic syndromes. Sci. Rep. 15, 8661 (2025).","journal-title":"Sci. Rep."},{"key":"55415_CR21","doi-asserted-by":"crossref","unstructured":"Vogado, L. et al. Diagnosis of leukaemia in blood slides based on a fine-tuned and highly generalisable deep learning model. Sensors 21 (2021).","DOI":"10.3390\/s21092989"},{"key":"55415_CR22","doi-asserted-by":"publisher","first-page":"153303381880278","DOI":"10.1177\/1533033818802789","volume":"17","author":"S Shafique","year":"2018","unstructured":"Shafique, S. & Tehsin, S. Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks. Technol. Cancer Res. Treat. 17, 1533033818802789 (2018).","journal-title":"Technol. Cancer Res. Treat."},{"key":"55415_CR23","doi-asserted-by":"publisher","first-page":"1396","DOI":"10.1109\/TVCG.2020.3030418","volume":"27","author":"ZJ Wang","year":"2021","unstructured":"Wang, Z. J. et al. CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization. IEEE Trans. Vis. Comput. Graph. 27, 1396\u20131406 (2021).","journal-title":"IEEE Trans. Vis. Comput. Graph"},{"key":"55415_CR24","doi-asserted-by":"crossref","unstructured":"Bukhari, M., Yasmin, S., Sammad, S. & Abd El-Latif, A. A. A Deep Learning framework for leukemia cancer detection in microscopic blood samples using squeeze and excitation learning. Math. Probl. Eng. 2801227 (2022).","DOI":"10.1155\/2022\/2801227"},{"key":"55415_CR25","doi-asserted-by":"crossref","unstructured":"Lavitt, F., Rijlaarsdam, D. J., van der Linden, D., Weglarz-Tomczak, E. & Tomczak, J. M. Deep learning and transfer learning for automatic cell counting in microscope images of human cancer cell lines. Appl. Sci. 11 (2021).","DOI":"10.3390\/app11114912"},{"key":"55415_CR26","doi-asserted-by":"publisher","unstructured":"Vogado, L. H. S. et al. Diagnosing leukemia in blood smear images using an ensemble of classifiers and pre-trained convolutional neural networks. In 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) 367\u2013373 (2017). https:\/\/doi.org\/10.1109\/SIBGRAPI.2017.55.","DOI":"10.1109\/SIBGRAPI.2017.55"},{"key":"55415_CR27","doi-asserted-by":"crossref","unstructured":"Shaheen, M. et al. Acute Myeloid Leukemia (AML) Detection Using AlexNet Model. Complexity 6658192 (2021).","DOI":"10.1155\/2021\/6658192"},{"key":"55415_CR28","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1002\/jemt.23139","volume":"81","author":"A Rehman","year":"2018","unstructured":"Rehman, A. et al. Classification of acute lymphoblastic leukemia using deep learning. Microsc Res. Tech. 81, 1310\u20131317 (2018).","journal-title":"Microsc Res. Tech."},{"key":"55415_CR29","doi-asserted-by":"crossref","unstructured":"Al-Obeidat, F. et al. Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis. Front Big Data 7, (2025).","DOI":"10.3389\/fdata.2024.1402926"},{"key":"55415_CR30","doi-asserted-by":"publisher","first-page":"2407","DOI":"10.3934\/era.2024110","volume":"32","author":"AA Hasanaath","year":"2024","unstructured":"Hasanaath, A. A. et al. Acute lymphoblastic leukemia detection using ensemble features from multiple deep CNN models. Electron. Res. Arch. 32, 2407\u20132423 (2024).","journal-title":"Electron. Res. Arch."},{"key":"55415_CR31","doi-asserted-by":"publisher","first-page":"4119","DOI":"10.1007\/s10278-025-01474-x","volume":"38","author":"H Almahdawi","year":"2025","unstructured":"Almahdawi, H., Akbas, A. & Rahebi, J. Deep Learning Neural Network Based on PSO for Leukemia Cell Disease Diagnosis from Microscope Images. J. Imaging Inf. Med. 38, 4119\u20134128 (2025).","journal-title":"J. Imaging Inf. Med."},{"key":"55415_CR32","doi-asserted-by":"publisher","first-page":"81","DOI":"10.33851\/JMIS.2019.6.2.81","volume":"6","author":"RK Gupta","year":"2019","unstructured":"Gupta, R. K., Kaur, M. & Manhas, J. Tissue Level Based Deep Learning Framework for Early Detection of Dysplasia in Oral Squamous Epithelium. J. Multimed Inf. Syst. 6, 81\u201386 (2019).","journal-title":"J. Multimed Inf. Syst."},{"key":"55415_CR33","doi-asserted-by":"publisher","first-page":"1801","DOI":"10.1007\/s11831-021-09639-x","volume":"29","author":"P Rani","year":"2022","unstructured":"Rani, P., Kotwal, S., Manhas, J., Sharma, V. & Sharma, S. Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments. Arch. Comput. Methods Eng. 29, 1801\u20131837 (2022).","journal-title":"Arch. Comput. Methods Eng."},{"key":"55415_CR34","doi-asserted-by":"publisher","first-page":"2469","DOI":"10.1007\/s11831-021-09660-0","volume":"29","author":"S Kotwal","year":"2022","unstructured":"Kotwal, S., Rani, P., Arif, T., Manhas, J. & Sharma, S. Automated Bacterial Classifications Using Machine Learning Based Computational Techniques: Architectures, Challenges and Open Research Issues. Arch. Comput. Methods Eng. 29, 2469\u20132490 (2022).","journal-title":"Arch. Comput. Methods Eng."},{"key":"55415_CR35","doi-asserted-by":"publisher","first-page":"108524","DOI":"10.1016\/j.compeleceng.2022.108524","volume":"105","author":"M Hussain","year":"2023","unstructured":"Hussain, M., Koundal, D. & Manhas, J. Deep learning-based diagnosis of disc degenerative diseases using MRI: A comprehensive review. Comput. Electr. Eng. 105, 108524 (2023).","journal-title":"Comput. Electr. Eng."},{"key":"55415_CR36","doi-asserted-by":"crossref","unstructured":"Nasir, M. U. et al. Hematologic cancer detection using white blood cancerous cells empowered with transfer learning and image processing. J. Healthc. Eng. 1406545 (2023).","DOI":"10.1155\/2023\/1406545"},{"key":"55415_CR37","doi-asserted-by":"publisher","first-page":"966","DOI":"10.3390\/biomedinformatics4020054","volume":"4","author":"R Haque","year":"2024","unstructured":"Haque, R. et al. Advancing Early Leukemia Diagnostics: A Comprehensive Study Incorporating Image Processing and Transfer Learning. BioMedInformatics 4, 966\u2013991 (2024).","journal-title":"BioMedInformatics"},{"key":"55415_CR38","doi-asserted-by":"publisher","first-page":"30201","DOI":"10.1007\/s11042-024-20312-w","volume":"84","author":"A Kumar","year":"2025","unstructured":"Kumar, A., Kumar, N., Kuriakose, J. & Sisodia, P. S. A deep transfer learning based approaches for the detection and classification of acute lymphocytic leukemia using microscopic images. Multimed Tools Appl. 84, 30201\u201330225 (2025).","journal-title":"Multimed Tools Appl."},{"key":"55415_CR39","doi-asserted-by":"publisher","first-page":"106023","DOI":"10.1016\/j.ijmedinf.2025.106023","volume":"203","author":"AA Soladoye","year":"2025","unstructured":"Soladoye, A. A. et al. Enhancing leukemia detection in medical imaging using deep transfer learning. Int. J. Med. Inf. 203, 106023 (2025).","journal-title":"Int. J. Med. Inf."},{"key":"55415_CR40","first-page":"84","volume":"6","author":"A Badruzzaman","year":"2023","unstructured":"Badruzzaman, A. & Arymurhty, A. M. A comparative study of convolutional neural network in detecting blast cells for diagnose acute myeloid leukemia. J. Electron. Electromed Eng. Med. Inf. 6, 84\u201391 (2023).","journal-title":"J. Electron. Electromed Eng. Med. Inf."},{"key":"55415_CR41","doi-asserted-by":"publisher","unstructured":"Hasan, M. M., Srizon, A. Y., Sayeed, A. & Hasan, M. A. M. Accurate recognition of leukemia sub-types by utilizing a transfer learned deep convolutional neural network. In 11th International Conference on Electrical and Computer Engineering (ICECE) 427\u2013430. https:\/\/doi.org\/10.1109\/ICECE51571.2020.9393031 (2020).","DOI":"10.1109\/ICECE51571.2020.9393031"},{"key":"55415_CR42","doi-asserted-by":"crossref","unstructured":"Bibi, N., Sikandar, M., Ud Din, I., Almogren, A. & Ali, S. IoMT-based automated detection and classification of leukemia using deep learning. J. Healthc. Eng. 6648574 (2020).","DOI":"10.1155\/2020\/6648574"},{"key":"55415_CR43","doi-asserted-by":"crossref","unstructured":"Abunadi, I. & Senan, E. M. Multi-method diagnosis of blood microscopic sample for early detection of acute lymphoblastic leukemia based on deep learning and hybrid techniques. Sensors 22, (2022).","DOI":"10.3390\/s22041629"},{"key":"55415_CR44","doi-asserted-by":"publisher","first-page":"13809","DOI":"10.1007\/s11042-024-19544-7","volume":"84","author":"GK Yenurkar","year":"2025","unstructured":"Yenurkar, G. K. et al. DeepLeuk: a convolutional neural network pre-trained model for microscopic cell images-Based leukemia Cancer analysis. Multimed Tools Appl. 84, 13809\u201313842 (2025).","journal-title":"Multimed Tools Appl."},{"key":"55415_CR45","doi-asserted-by":"publisher","first-page":"100397","DOI":"10.1016\/j.array.2025.100397","volume":"26","author":"D Muduli","year":"2025","unstructured":"Muduli, D. et al. Deep learning-based detection and classification of acute lymphoblastic leukemia with explainable AI techniques. Array 26, 100397 (2025).","journal-title":"Array"},{"key":"55415_CR46","doi-asserted-by":"crossref","unstructured":"Kizi, R. F. O., Armand, T. P. T. & Kim, H. C. A review of deep learning techniques for leukemia cancer classification based on blood smear images. Appl. Biosci. 4, (2025).","DOI":"10.3390\/applbiosci4010009"},{"key":"55415_CR47","doi-asserted-by":"crossref","unstructured":"Tanwar, V., Sharma, B., Yadav, D. P. & Dwivedi, A. D. Enhancing blood cell diagnosis using hybrid residual and dual block transformer network. Bioengineering 12 (2025).","DOI":"10.3390\/bioengineering12020098"},{"key":"55415_CR48","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/978-3-031-71602-7_10","volume-title":"Artificial Neural Networks in Pattern Recognition","author":"K Ben-Suliman","year":"2024","unstructured":"Ben-Suliman, K. & Krzy\u017cak, A. Vision Transformer Features-Based Leukemia Classification. in Artificial Neural Networks in Pattern Recognition (ed (eds Suen, C. Y. et al.) 111\u2013120 (Springer Nature Switzerland, Cham, doi:https:\/\/doi.org\/10.1007\/978-3-031-71602-7_10. (2024)."},{"key":"55415_CR49","doi-asserted-by":"publisher","first-page":"107583","DOI":"10.1016\/j.cmpb.2023.107583","volume":"237","author":"G Yang","year":"2023","unstructured":"Yang, G. et al. Efficient diagnosis of hematologic malignancies using bone marrow microscopic images: A method based on MultiPathGAN and MobileViTv2. Comput. Methods Programs Biomed. 237, 107583 (2023).","journal-title":"Comput. Methods Programs Biomed."},{"key":"55415_CR50","doi-asserted-by":"publisher","unstructured":"Kumar, B. A. & Bansal, M. A. Transfer learning approach with MobileNetV2 for Parkinsons Disease detection using hand-drawings. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) 1\u20137. https:\/\/doi.org\/10.1109\/ICCCNT56998.2023.10307641 (2023).","DOI":"10.1109\/ICCCNT56998.2023.10307641"},{"key":"55415_CR51","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s00521-024-10563-x","volume":"37","author":"BA Kumar","year":"2025","unstructured":"Kumar, B. A. & Misra, N. K. Colon cancer classification and detection by novel CMNV2 model and methods of deep learning. Neural Comput. Appl. 37, 25\u201341 (2025).","journal-title":"Neural Comput. Appl."},{"key":"55415_CR52","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1007\/978-981-10-8660-1_61","volume-title":"Smart and Innovative Trends in Next Generation Computing Technologies","author":"RR Choudhary","year":"2018","unstructured":"Choudhary, R. R., Sharma, S. & Meena, G. Detection of Leukemia in Human Blood Samples Through Image Processing. in Smart and Innovative Trends in Next Generation Computing Technologies (eds (eds Bhattacharyya, P., Sastry, H. G., Marriboyina, V. & Sharma, R.) 824\u2013834 (Springer, Singapore, doi:https:\/\/doi.org\/10.1007\/978-981-10-8660-1_61. (2018)."},{"key":"55415_CR53","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s44196-023-00186-w","volume":"16","author":"D Yang","year":"2023","unstructured":"Yang, D., Ngoc, K. M., Shin, I. & Hwang, M. DPReLU: Dynamic Parametric Rectified Linear Unit and Its Proper Weight Initialization Method. Int. J. Comput. Intell. Syst. 16, 11 (2023).","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"55415_CR54","doi-asserted-by":"crossref","unstructured":"Ther, A., Kumar Pandit, B., Ganguly, A., Chakraborty, A. & Banerjee, A. Resource-efficient VLSI architecture of softmax activation function for real-time inference in deep learning applications. In 2023 International Symposium on Devices, Circuits and Systems (ISDCS) vol. 1 01\u201305 (2023).","DOI":"10.1109\/ISDCS58735.2023.10153520"},{"key":"55415_CR55","doi-asserted-by":"publisher","first-page":"012024","DOI":"10.1088\/1757-899X\/844\/1\/012024","volume":"844","author":"SA Sanchez","year":"2020","unstructured":"Sanchez, S. A., Romero, H. J. & Morales, A. D. A review: Comparison of performance metrics of pretrained models for object detection using the TensorFlow framework. IOP Conf. Ser. Mater. Sci. Eng. 844, 012024 (2020).","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"55415_CR56","doi-asserted-by":"publisher","first-page":"4157","DOI":"10.1007\/s11760-024-03062-y","volume":"18","author":"KD Shree","year":"2024","unstructured":"Shree, K. D. & Logeswari, S. ODRNN: optimized deep recurrent neural networks for automatic detection of leukaemia. Signal. Image Video Process. 18, 4157\u20134173 (2024).","journal-title":"Signal. Image Video Process."},{"key":"55415_CR57","doi-asserted-by":"publisher","first-page":"110860","DOI":"10.1016\/j.compbiomed.2025.110860","volume":"196","author":"L Mei","year":"2025","unstructured":"Mei, L. et al. High-efficiency spatially guided learning network for lymphoblastic leukemia detection in bone marrow microscopy images. Comput. Biol. Med. 196, 110860 (2025).","journal-title":"Comput. Biol. Med."},{"key":"55415_CR58","doi-asserted-by":"publisher","first-page":"107840","DOI":"10.1016\/j.bspc.2025.107840","volume":"107","author":"R Saikia","year":"2025","unstructured":"Saikia, R. et al. VNLU-Net: Visual Network with Lightweight Union-net for Acute Myeloid Leukemia Detection on Heterogeneous Dataset. Biomed. Signal. Process. Control. 107, 107840 (2025).","journal-title":"Biomed. Signal. Process. Control"},{"key":"55415_CR59","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1016\/j.isatra.2024.12.043","volume":"158","author":"PK Das","year":"2025","unstructured":"Das, P. K., Meher, S., Rath, A. & Panda, G. An efficient deep learning system for automatic detection of Acute Lymphoblastic Leukemia. ISA Trans. 158, 488\u2013496 (2025).","journal-title":"ISA Trans."},{"key":"55415_CR60","doi-asserted-by":"publisher","first-page":"101618","DOI":"10.1016\/j.imu.2025.101618","volume":"53","author":"K Pervez","year":"2025","unstructured":"Pervez, K., Sohail, S. I., Parwez, F. & Zia, M. A. Towards trustworthy AI-driven leukemia diagnosis: A hybrid Hierarchical Federated Learning and explainable AI framework. Inf. Med. Unlocked. 53, 101618 (2025).","journal-title":"Inf. Med. Unlocked"},{"key":"55415_CR61","doi-asserted-by":"publisher","first-page":"118176","DOI":"10.1016\/j.measurement.2025.118176","volume":"256","author":"E Ba\u015faran","year":"2025","unstructured":"Ba\u015faran, E., \u00c7elik, G. & To\u011fa\u00e7ar, M. Regionally focused neural-coder model designed for the diagnosis of acute lymphoblastic leukemia disease. Measurement 256, 118176 (2025).","journal-title":"Measurement"},{"key":"55415_CR62","doi-asserted-by":"crossref","unstructured":"Makem, M., Tamas, L. & Bu\u0219oniu, L. A reliable approach for identifying acute lymphoblastic leukemia in microscopic imaging. Front Artif. Intell 8 (2025).","DOI":"10.3389\/frai.2025.1620252"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-55415-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-55415-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-55415-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T13:02:55Z","timestamp":1786366975000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-55415-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,30]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["55415"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-55415-x","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,30]]},"assertion":[{"value":"14 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"24693"}}